TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics

We present TIPP3 and TIPP3-fast, new tools for abundance profiling in metagenomic datasets. Like its predecessor, TIPP2, the TIPP3 pipeline uses a maximum likelihood approach to place reads into labeled taxonomies using marker genes, but it achieves superior accuracy to TIPP2 by enabling the use of much larger taxonomies through improved algorithmic techniques. We show that TIPP3 is generally more accurate than leading methods for abundance profiling in two important contexts: when reads come from genomes not already in a public database (i.e., novel genomes) and when reads contain sequencing errors. We also show that TIPP3-fast has slightly lower accuracy than TIPP3, but is also generally more accurate than other leading methods and uses a small fraction of TIPP3's runtime. Additionally, we highlight the potential benefits of restricting abundance profiling methods to those reads that map to marker genes (i.e., using a filtered marker-gene based analysis), which we show typically improves accuracy. TIPP3 is freely available at https://github.com/c5shen/TIPP3.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-04-04
DOI
https://doi.org/10.1371/journal.pcbi.1012593
Citations
2
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
1.61

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article

TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics

Chengze Shen, Tandy Warnow, Mihai Pop, Eleanor Wedell
2 citations
PLoS Computational Biology
Genomics and Phylogenetic Studies
1.61
article

TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics

Chengze Shen, Tandy Warnow, Mihai Pop, Eleanor Wedell
article en
2 citations

Abstract

We present TIPP3 and TIPP3-fast, new tools for abundance profiling in metagenomic datasets. Like its predecessor, TIPP2, the TIPP3 pipeline uses a maximum likelihood approach to place reads into labeled taxonomies using marker genes, but it achieves superior accuracy to TIPP2 by enabling the use of much larger taxonomies through improved algorithmic techniques. We show that TIPP3 is generally more accurate than leading methods for abundance profiling in two important contexts: when reads come from genomes not already in a public database (i.e., novel genomes) and when reads contain sequencing errors. We also show that TIPP3-fast has slightly lower accuracy than TIPP3, but is also generally more accurate than other leading methods and uses a small fraction of TIPP3's runtime. Additionally, we highlight the potential benefits of restricting abundance profiling methods to those reads that map to marker genes (i.e., using a filtered marker-gene based analysis), which we show typically improves accuracy. TIPP3 is freely available at https://github.com/c5shen/TIPP3.

PLoS Computational BiologyVol. 21(4)
University of Illinois Urbana-Champaign (US), University of Maryland, College Park (US)
National Science Foundation
Openalex Percentile: Top 18%
Genomics and Phylogenetic Studies
1.61
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